This paper explores what a quantitative trading algorithm is, and how one can be developed and evaluated through statistical models and simulations.&nbsp; <div> This independent research paper is exploratory and aims to create a basic understanding of how different models perform in practice. It is not to present a finished trading strategy. The historical price data for which the developed algorithms are based on is S&amp;P 500 index fund (SPY) from 2021-2026.&nbsp; </div> <div> <br> </div> <div> The five versions of the trading algorithms were developed and evaluated in Python. Each version introduced new tools, from basic market trends to an AI-driven sentiment analysis. The results show that model performance varies significantly through different market conditions, highlighting the importance of flexibility in an algorithm.&nbsp; <div> <br> </div> <div> The final model achieved a Sharpe ratio of 3.32 and a maximum drawdown of 5.43%. These results are promising but should be interpreted with caution. These models have only been tested on historical data, and historical performance can never be used as a guarantee for future outcomes. </div> </div>